Brandon Driscoll

A Novel Minimally Invasive Technique to Create a Rabbit VX2 Lung Tumor Model for Nano-Sized Image Contrast and Interventional Studies

Background The rabbit VX2 lung cancer model is a large animal model useful for preclinical lung cancer imaging and interventional studies. However, previously reported models had issues in terms of invasiveness of tumor inoculation, control of tumor aggressiveness and incidence of complications. Purpose We aimed to develop a minimally invasive rabbit VX2 lung cancer model suitable for imaging and transbronchial interventional studies. Methods New Zealand white rabbits and VX2 tumors were used in the study. An ultra-thin bronchoscope was inserted through a miniature laryngeal mask airway into the bronchus. Different numbers of VX2 tumor cells were selectively inoculated into the lung parenchyma or subcarinal mediastinum to create a uniform tumor with low incidence of complications. The model was characterized by CT, FDG-PET, and endobronchial ultrasound (EBUS). Liposomal dual-modality contrast agent was used to evaluate liposome drug delivery system in this model. Results Both peripheral and mediastinal lung tumor models were created. The tumor making success rate was 75.8% (25/33) in the peripheral lung tumor model and 60% (3/5) in the mediastinal tumor model. The group of 1.0×106 of VX2 tumor cells inoculation showed a linear growth curve with less incidence of complications. Radial probe EBUS visualized the internal structure of the tumor and the size measurement correlated well with CT measurements (r2 = 0.98). Over 7 days of continuous enhancement of the lung tumor by liposomal contrast in the lung tumor was confirmed both CT and fluorescence imaging. Conclusion Our minimally invasive bronchoscopic rabbit VX2 lung cancer model is an ideal platform for lung cancer imaging and preclinical bronchoscopic interventional studies.

Development of a dynamic quality assurance testing protocol for multisite clinical trial DCE‐CT accreditation

PURPOSE Credentialing can have an impact on whether or not a clinical trial produces useful quality data that is comparable between various institutions and scanners. With the recent increase of dynamic contrast enhanced-computed tomography (DCE-CT) usage as a companion biomarker in clinical trials, effective quality assurance, and control methods are required to ensure there is minimal deviation in the results between different scanners and protocols at various institutions. This paper attempts to address this problem by utilizing a dynamic flow imaging phantom to develop and evaluate a DCE-CT quality assurance (QA) protocol. METHODS A previously designed flow phantom, capable of producing predictable and reproducible time concentration curves from contrast injection was fully validated and then utilized to design a DCE-CT QA protocol. The QA protocol involved a set of quantitative metrics including injected and total mass error, as well as goodness of fit comparison to the known truth concentration curves. An additional region of interest (ROI) sensitivity analysis was also developed to provide additional details on intrascanner variability and determine appropriate ROI sizes for quantitative analysis. Both the QA protocol and ROI sensitivity analysis were utilized to test variations in DCE-CT results using different imaging parameters (tube voltage and current) as well as alternate reconstruction methods and imaging techniques. The developed QA protocol and ROI sensitivity analysis was then applied at three institutions that were part of clinical trial involving DCE-CT and results were compared. RESULTS The inherent specificity of robustness of the phantom was determined through calculation of the total intraday variability and determined to be less than 2.2±1.1% (total calculated output contrast mass error) with a goodness of fit (R2) of greater than 0.99±0.0035 (n=10). The DCE-CT QA protocol was capable of detecting significant deviations from the expected phantom result when scanning at low mAs and low kVp in terms of quantitative metrics (Injected Mass Error 15.4%), goodness of fit (R2) of 0.91, and ROI sensitivity (increase in minimum input function ROI radius by 146±86%). These tests also confirmed that the ASIR reconstruction process was beneficial in reducing noise without substantially increasing partial volume effects and that vendor specific modes (e.g., axial shuttle) did not significantly affect the phantom results. The phantom and QA protocol were finally able to quickly (<90 min) and successfully validate the DCE-CT imaging protocol utilized at the three separate institutions of a multicenter clinical trial; thereby enhancing the confidence in the patient data collected. CONCLUSIONS A DCE QA protocol was developed that, in combination with a dynamic multimodality flow phantom, allows the intrascanner variability to be separated from other sources of variability such as the impact of injection protocol and ROI selection. This provides a valuable resource that can be utilized at various clinical trial institutions to test conformance with imaging protocols and accuracy requirements as well as ensure that the scanners are performing as expected for dynamic scans.

Automated Voxel-Based Analysis of Volumetric Dynamic Contrast-Enhanced CT Data Improves Measurement of Serial Changes in Tumor Vascular Biomarkers

OBJECTIVES Development of perfusion imaging as a biomarker requires more robust methodologies for quantification of tumor physiology that allow assessment of volumetric tumor heterogeneity over time. This study proposes a parametric method for automatically analyzing perfused tissue from volumetric dynamic contrast-enhanced (DCE) computed tomography (CT) scans and assesses whether this 4-dimensional (4D) DCE approach is more robust and accurate than conventional, region-of-interest (ROI)-based CT methods in quantifying tumor perfusion with preliminary evaluation in metastatic brain cancer. METHODS AND MATERIALS Functional parameter reproducibility and analysis of sensitivity to imaging resolution and arterial input function were evaluated in image sets acquired from a 320-slice CT with a controlled flow phantom and patients with brain metastases, whose treatments were planned for stereotactic radiation surgery and who consented to a research ethics board-approved prospective imaging biomarker study. A voxel-based temporal dynamic analysis (TDA) methodology was used at baseline, at day 7, and at day 20 after treatment. The ability to detect changes in kinetic parameter maps in clinical data sets was investigated for both 4D TDA and conventional 2D ROI-based analysis methods. RESULTS A total of 7 brain metastases in 3 patients were evaluated over the 3 time points. The 4D TDA method showed improved spatial efficacy and accuracy of perfusion parameters compared to ROI-based DCE analysis (P<.005), with a reproducibility error of less than 2% when tested with DCE phantom data. Clinically, changes in transfer constant from the blood plasma into the extracellular extravascular space (Ktrans) were seen when using TDA, with substantially smaller errors than the 2D method on both day 7 post radiation surgery (±13%; P<.05) and by day 20 (±12%; P<.04). Standard methods showed a decrease in Ktrans but with large uncertainty (111.6 ± 150.5) %. CONCLUSIONS Parametric voxel-based analysis of 4D DCE CT data resulted in greater accuracy and reliability in measuring changes in perfusion CT-based kinetic metrics, which have the potential to be used as biomarkers in patients with metastatic brain cancer.

A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides quantitative metrics (e.g. Ktrans, ve) via pharmacokinetic models. We tested inter-algorithm variability in these quantitative metrics with 11 published DCE-MRI algorithms, all implementing Tofts-Kermode or extended Tofts pharmacokinetic models. Digital reference objects (DROs) with known Ktrans and ve values were used to assess performance at varying noise levels. Additionally, DCE-MRI data from 15 head and neck squamous cell carcinoma patients over 3 time-points during chemoradiotherapy were used to ascertain Ktrans and ve kinetic trends across algorithms. Algorithms performed well (less than 3% average error) when no noise was present in the DRO. With noise, 87% of Ktrans and 84% of ve algorithm-DRO combinations were generally in the correct order. Low Krippendorff’s alpha values showed that algorithms could not consistently classify patients as above or below the median for a given algorithm at each time point or for differences in values between time points. A majority of the algorithms produced a significant Spearman correlation in ve of the primary gross tumor volume with time. Algorithmic differences in Ktrans and ve values over time indicate limitations in combining/comparing data from distinct DCE-MRI model implementations. Careful cross-algorithm quality-assurance must be utilized as DCE-MRI results may not be interpretable using differing software.Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides quantitative metrics (e.g. Ktrans, ve) via pharmacokinetic models. We tested inter-algorithm variability in these quantitative metrics with 11 published DCE-MRI algorithms, all implementing Tofts-Kermode or extended Tofts pharmacokinetic models. Digital reference objects (DROs) with known Ktrans and ve values were used to assess performance at varying noise levels. Additionally, DCE-MRI data from 15 head and neck squamous cell carcinoma patients over 3 time-points during chemoradiotherapy were used to ascertain Ktrans and ve kinetic trends across algorithms. Algorithms performed well (less than 3% average error) when no noise was present in the DRO. With noise, 87% of Ktrans and 84% of ve algorithm-DRO combinations were generally in the correct order. Low Krippendorff’s alpha values showed that algorithms could not consistently classify patients as above or below the median for a given algorithm at each time point or for differences in values between time points. A majority of the algorithms produced a significant Spearman correlation in ve of the primary gross tumor volume with time. Algorithmic differences in Ktrans and ve values over time indicate limitations in combining/comparing data from distinct DCE-MRI model implementations. Careful cross-algorithm quality-assurance must be utilized as DCE-MRI results may not be interpretable using differing software.

Feasibility of 4D perfusion CT imaging for the assessment of liver treatment response following SBRT and sorafenib

Objectives To evaluate the feasibility of 4-dimensional perfusion computed tomography (CT) as an imaging biomarker for patients with hepatocellular carcinoma and metastatic liver disease. Methods and materials Patients underwent volumetric dynamic contrast-enhanced CT on a 320-slice scanner before and during stereotactic body radiation therapy and sorafenib, and at 1 and 3 months after treatment. Quiet free breathing was used in the CT acquisition and multiple techniques (rigid or deformable registration as well as outlier removal) were applied to account for residual liver motion. Kinetic modeling was performed on a voxel-by-voxel basis in the gross tumor volume and normal liver resulting in 3-dimensional parameter maps of blood perfusion, capillary permeability, blood volume, and mean transit time. Perfusion characteristics in the tumor and adjacent liver were correlated with radiation dose distributions to evaluate dose-response. Paired t tests assessed change in spatial and histogram parameters from baseline to different time points during and after treatment. Technique reproducibility as well as the impact of arterial and portal vein input functions was also investigated using intra- and inter-subject variance and Bland-Altman analysis. Results Quantitative perfusion parameters were reproducible (±5.7%; range, 2%-10%) depending on tumor/normal liver type and kinetic parameter. Statistically significant reductions in tumor perfusion were measurable over the course of treatment and as early as 1 week after sorafenib administration (P < .05). Marked liver parenchyma perfusion reduction was seen with a strong dose-response effect (R2 = 0.95) that increased significantly over the course treatment. Conclusions The proposed methodology demonstrated feasibility of evaluating spatiotemporal changes in liver tumor perfusion and normal liver function following antiangiogenic therapy and radiation treatment warranting further evaluation of biomarker prognostication.

Multimodality Functional Imaging in Radiation Therapy Planning: Relationships between Dynamic Contrast-Enhanced MRI, Diffusion-Weighted MRI, and 18F-FDG PET

OBJECTIVES Biologically guided radiotherapy needs an understanding of how different functional imaging techniques interact and link together. We analyse three functional imaging techniques that can be useful tools for achieving this objective. MATERIALS AND METHODS The three different imaging modalities from one selected patient are ADC maps, DCE-MRI, and 18F-FDG PET/CT, because they are widely used and give a great amount of complementary information. We show the relationship between these three datasets and evaluate them as markers for tumour response or hypoxia marker. Thus, vascularization measured using DCE-MRI parameters can determine tumour hypoxia, and ADC maps can be used for evaluating tumour response. RESULTS ADC and DCE-MRI include information from 18F-FDG, as glucose metabolism is associated with hypoxia and tumour cell density, although 18F-FDG includes more information about the malignancy of the tumour. The main disadvantage of ADC maps is the distortion, and we used only low distorted regions, and extracellular volume calculated from DCE-MRI can be considered equivalent to ADC in well-vascularized areas. CONCLUSION A dataset for achieving the biologically guided radiotherapy must include a tumour density study and a hypoxia marker. This information can be achieved using only MRI data or only PET/CT studies or mixing both datasets.

Pulmonary Tumor Measurements from X-Ray Computed Tomography in One, Two, and Three Dimensions

RATIONALE AND OBJECTIVES We evaluated the accuracy and reproducibility of three-dimensional (3D) measurements of lung phantoms and patient tumors from x-ray computed tomography (CT) and compared these to one-dimensional (1D) and two-dimensional (2D) measurements. MATERIALS AND METHODS CT images of three spherical and three irregularly shaped tumor phantoms were evaluated by three observers who performed five repeated measurements. Additionally, three observers manually segmented 29 patient lung tumors five times each. Follow-up imaging was performed for 23 tumors and response criteria were compared. For a single subject, imaging was performed on nine occasions over 2 years to evaluate multidimensional tumor response. To evaluate measurement accuracy, we compared imaging measurements to ground truth using analysis of variance. For estimates of precision, intraobserver and interobserver coefficients of variation and intraclass correlations (ICC) were used. Linear regression and Pearson correlations were used to evaluate agreement and tumor response was descriptively compared. RESULTS For spherical shaped phantoms, all measurements were highly accurate, but for irregularly shaped phantoms, only 3D measurements were in high agreement with ground truth measurements. All phantom and patient measurements showed high intra- and interobserver reproducibility (ICC >0.900). Over a 2-year period for a single patient, there was disagreement between tumor response classifications based on 3D measurements and those generated using 1D and 2D measurements. CONCLUSION Tumor volume measurements were highly reproducible and accurate for irregular, spherical phantoms and patient tumors with nonuniform dimensions. Response classifications obtained from multidimensional measurements suggest that 3D measurements provide higher sensitivity to tumor response.

SU-D-303-02: Impact of Arterial Input Function Selection and T10 Correction On DCE-MRI Tumour Response Prediction Using Compared to Volumetric DCE CT

Purpose: To evaluate the impact of individualized magnitude and phase signal arterial input function (AIF) measurements as well as voxel-based pre-contrast T10 relaxation on tumour perfusion metrics from DCE-MRI compared to DCE-CT using a common 4D temporal dynamic analysis (TDA) method. Methods: Nine patients with 13 brain metastases underwent volumetric DCE-CT (Toshiba, Aquilion ONE) and DCE-MRI (IMRIS 3T Verio) at baseline then 7 and 21 days post-radiosurgery. Voxel-based whole brain TDA was performed on all data using in-house software producing kinetic parameters AUC, Ktrans, Kep, and Vb (using the Modified Tofts model). AIF susceptibility was investigated in DCE-CT by selecting the AIF (from internal carotid artery) or VIF (from Sagittal Sinus). In DCE-MRI an individual Magnitude or Phase-based AIF (Sagittal Sinus) was compared to population-based AIF together with susceptibility to voxel-based T10 maps instead of a constant of 2400 msec. Absolute DCE-MRI values were compared to DCE-CT by Pearson correlation. Results: No significant difference in median Ktrans (0.048 +/−0.03 s-1) or AUC (2785.5 +/−1143.6 HU.s) was found between individual AIF and VIF-based DCE CT analyses. Using individual Magnitude VIF or Phase-based AIF for DCE-MRI (T10 2400 ms) resulted in higher Ktrans values (0.181 +/−0.11 vs. 0.121 +/−0.099 s-1). This is likely resulting from the smaller AIF peak since the population AIF (which more closely resembles CT) correlates better to DCE-CT metrics. Using voxel-based T10 maps caused further statistically-significant increase in Ktrans and AUC (p<0.0006) that could be contributed to a lower median T10 value (1572 +/−594, n=41). Conclusion: This preliminary data highlights the stability of DCE-CT calculations as well as susceptibility of DCE-MRI Ktrans measurements to various imaging factors, including AIF selection and T10 values used for modeling. Efforts to improve voxel-based T10 map calculations are being explored to further explain discrepancies between analysis methods. Brain Tumor Foundation of Canada and Ontario Institute of Cancer Research

Publisher Correction: A Multi-Institutional Comparison of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Parameter Calculations (Scientific Reports (2017) DOI: 10.1038/s41598-017-11554-w)

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Erratum: Dynamic contrast-enhanced magnetic resonance imaging for head and neck cancers

This corrects the article DOI: 10.1038/sdata.2018.8.